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The hydrogen engine is one of the promising technologies that enables carbon-neutral mobility, especially in heavy-duty on- or off-road applications. In this paper, a methodological procedure for the design of the combustion system of a hydrogen-fueled, direct injection spark ignited commercial vehicle engine is described. In a preliminary step, the ability of the commercial 3D computational fluid dynamics (CFD) code AVL FIRE classic to reproduce the characteristics of the gas jet, introduced into a quiescent environment by a dedicated H2 injector, is established. This is based on two parts: Temporal and numerical discretization sensitivity analyses ensure that the spatial and temporal resolution of the simulations is adequate, and comparisons to a comprehensive set of experiments demonstrate the accuracy of the simulations. The measurements used for this purpose rely on the well-known schlieren technique and use helium as a safe substitute for H2.

In order to improve the efficiency of passenger cars, developments focus on decreasing their aerodynamic drag, part of which is caused by cooling air. Thus, car manufacturers try to seal the cooling air path to prevent leakage flows. Nevertheless, gaps between the single components of the cooling air path widen due to the deformation of components under aerodynamic load. For simulating the cooling airflow utilization ratio (CAUR), computational fluid dynamics (CFD) simulations are used, which neglect component deformation. In this paper, a computational method aiming at sufficient gap resolution and determining the CAUR of passenger cars under the consideration of component deformation is developed. Therefore, a partitioned approach of fluid structure interaction (FSI) simulations is used. The fluid field is simulated in OpenFOAM, whereas the structural simulations are conducted using Pam-Crash.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

Ducted Fuel Injection (DFI) engines have emerged as a promising technology in the pursuit of a clean and efficient combustion process. This article aims at elucidating the effect of piston geometry on the engine performance and emissions of a metal DFI engine. Three different types of pistons were investigated and the main piston design features including the piston bowl diameter, piston bowl slope angle, duct angle and the injection nozzle position were examined. To achieve the target, computational fluid dynamics (CFD) simulations were conducted coupled to a reduced chemical kinetics mechanism. Extensive validations were performed against the measured data from a conventional diesel engine. To calibrate the soot model, genetic algorithm and machine learning methods were utilized. The simulation results highlight the pivotal role played by piston bowl diameter and fuel injection angle in controlling soot emissions of a DFI engine.

The modern automotive industry is facing challenges of ever-increasing complexity in the electrified powertrain era. On-board diagnostic (OBD) systems must be thoroughly validated and calibrated through many iterations to function effectively and meet the regulation standards. Their development and design process are more complex when prototype hardware is not available and therefore virtual testing is a prominent solution, including Software-in-the-loop (SiL) and Hardware-in-the-loop (HIL) simulations. Virtual prototype testing relying on real-time simulation models is necessary to design and test new era’s OBD systems quickly and in scale. The new fuel cell powertrain involves new and preciously unexplored fail modes. To make the system robust, simulations are required to be carried out to identify different fails.

Ammonia has emerged as a promising carbon-free alternative fuel for internal combustion engines (ICE), particularly in large-bore engine applications. However, integrating ammonia into conventional engines presents challenges, prompting the exploration of innovative combustion strategies like dual-fuel combustion. Nitrous oxide (N2O) emissions have emerged as a significant obstacle to the widespread adoption of ammonia in ICE. Various studies suggest that combining exhaust gas recirculation (EGR) with adjustments in inlet temperature and diesel injection timing can effectively mitigate nitrogen oxides (NOx) emissions across diverse operating conditions in dual-fuel diesel engines.

The transition from ICE to electric power trains in new vehicles along with the application of advanced active and passive noise reduction solutions has intensified the perception of noise sources not directly linked to the propulsion system. This includes road noise as amplified by the tire cavity resonance. This resonance mainly depends on tire geometry, air temperature inside the tire and vehicle speed and is increasingly audible for larger wheels and heavier vehicles, as they are typical for current electrical SUV designs. Active technologies can be applied to significantly reduce narrow band tire cavity noise with low costs and minimal weight increase. Like ANC systems for ICE powertrains, they make use of the audio system in the vehicle. In this paper, a novel low-cost system for road induced tire cavity noise control (RTNC) is presented that reduces the tire cavity resonance noise inside a car cabin.

This paper presents the novel active vibration control (AVC) system that controls vehicle body vibration to reduce the structural borne road noise. As a result of vehicle noise testing in an electric vehicle, the predominant frequency of vehicle body vibration that worsens interior noise is in the range of 150-250Hz. Such vibration in that frequency range, commonly masked in engine vibrations, are hard to neglect for electric vehicles. The vibration source of that frequency is the resonance of tire cavity mode. Resonator or absorption material has been applied inside the tire for the control of cavity noise as a passive method. They require an increment of weight and cost. Therefore, a novel method is necessary. The vibration amplified by resonance of cavity mode is transferred to the vehicle body throughout the suspension system. To reduce the vibration, AVC system is applied to the suspension mount.

The engine acoustic character has always represented the product DNA, owing to its strong correlation with in-cylinder pressure gradient, components design and perceived quality. Best practice for engine acoustic characterization requires the employment of a hemi-anechoic chamber, a significant number of sensors and special acoustic insulation for engine ancillaries and transmission. This process is highly demanding in terms of cost and time due to multiple engine working points to be tested and consequent data post-processing. Since Neural Networks potentially predicting capabilities are apparently un-exploited in this research field, the following paper provides a tool able to acoustically estimate engine performance, processing system inputs (e.g. Injected Fuel, Rail Pressure) thanks to the employment of Multi Layer Perceptron (MLP, a feed forward Network working in stationary points).

While conventional methods like classical Transfer Path Analysis (TPA), Multiple Coherence Analysis (MCA), Operational Deflection Shape (ODS), and Modal Analysis have been widely used for road noise reduction, component-TPA from Model Based System Engineering (MBSE) is gaining attention for its ability to efficiently develop complex mobility systems. In this research, we propose a method to achieve road noise targets in the early stage of vehicle development using component-level TPA based on the blocked force method. An important point is to ensure convergence of measured test results (e.g. sound pressure at driver ear) and simulation results from component TPA. To conduct component-TPA, it is essential to have an independent tire model consisting of tire blocked force and tire Frequency Response Function (FRF), as well as full vehicle FRF and vehicle hub FRF.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

With the electrification of powertrains, the noise level inside vehicles reach high levels of silence. The dominant engine noise found in traditional vehicles is now replaced by other sources of noise such as rolling noise and aeroacoustic noise. These noises are encountered during driving on roads and highways and can cause significant fatigue during long journeys. Regarding aeroacoustic phenomena, the noise transmitted into the cabin is the result of both turbulent pressure and acoustic pressure created by the airflow. Even though it is lower in level, the acoustic pressure induces most of the noise perceived by the occupants. Its wavelength is closer to the characteristic vibration wavelengths of the glass, making its propagation more efficient through the vehicle's windows. The accurate modeling of these phenomena requires the coupling of high-frequency computational fluid dynamics (CFD) simulations and vibro-acoustic simulations.

Centrifugal fans are applied in many industrial and civil applications, such as manufacturing processes and building HVAC systems. They can also be found in automotive applications. Noise-reduction mea- sures for centrifugal fans are often challenging to establish, as acous- tic performance may be considered a tertiary purchase criterion after energetic efficiency and price. Nonetheless, their versatile application raises the demand for noise control. In a low-Mach-number centrifugal fan, acoustic waves are predominantly excited by aerodynamic fluctu- ations in the flow field and transmit to the exterior via the housing and duct walls. The scientific literature documents numerous mech- anisms that cause flow-induced sound generation, even though only some are considered well-understood. Numerical simulation methods are widely used to gather spatially high-resolved insights into physical fields.

In the acoustic study of the interior noise of a vehicle, whether for structure-borne or air-borne excitations, knowing which areas contribute the most to interior noise and therefore should be treated as a priority, is the main goal of the engineer in charge of the NVH. Very often these areas are numerous, located in different regions of the vehicle and contribute at different frequencies to the overall sound pressure level. This has led to the development of several “Panel Contribution Analysis” (PCA) experimental techniques. For example, a well-known technique is the masking technique, which consists of applying a “maximum package” (i.e., a package with very high sound insulation) to the panels outside of the area whose contribution has to be measured. This technique is pragmatic but rather cumbersome to implement. In addition, it significantly modifies the dynamics and internal acoustics of the vehicle.

The NVH performance of electric vehicles is a key indicator of vehicle quality, being the structure-borne transmission predominating at low frequencies. Many issues are typically generated by high vibrations, transmitted through different paths, and then radiated acoustically into the cabin. A combined analysis, with both finite-element and multi-body models, enables to predict the interior vehicle noise and vibration earlier in the development phases, to reduce the development time and moreover to optimize components with an increased efficiency level. In the present work, a simulation of a Hyundai electric vehicle has been performed in IDIADA VPG with a full vehicle multibody (MBD) model, followed by vibration/acoustic simulations with a Finite elements model (FEM) in MSC. Nastran to analyze the comfort. Firstly, a full vehicle MBD model has been developed in MSC. ADAMS/Car including representative flexible bodies (generated from FEM part models).

Finite element (FE) based simulations for fully trimmed bodies are a key tool in the automotive industry to predict and understand the Noise, Vibration and Harshness (NVH) behavior of a complete car. While structural and acoustic transfer functions are nowadays straight-forward to obtain from such models, the comprehensive understanding of the intrinsic behavior of the complete car is more complex to achieve, in particular when it comes to the contribution of each sub-part to the global response. This paper proposes a complete target cascading process, which first assesses which sub-part of the car is the most contributing to the interior noise, then decomposes the total structure-borne acoustic transfer function into several intermediate transfer functions, allowing to better understand the effect of local design changes.